Emerging Infectious Diseases Volume 26, Number 7—July 2020
Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released. Accessed online: May 10, 2020
This article shows the data from Wuhan and 26 provinces. Described in Figure 3C, from January 18th to 26th, the growth of new cases in areas outside of Hubei, grew rapidly (over only 8 days) and exponentially. These data further support the public health imperative of limiting social interactions in areas of high SARS-CoV-2 transmission and discouraging travel from these areas to areas of no or low transmission.
The authors also underscore that while they have calculated an R0 , they have made a series of assumptions that may or may not prove correct. They summarize:
“The values estimated have important implications for predicting the effects of pharmaceutical and nonpharmaceutical interventions. For example, the threshold for combined vaccine efficacy and herd immunity needed for disease extinction is calculated as 1 – 1/R0 . At R0 = 2.2, this threshold is only 55%. But at R0 = 5.7, this threshold rises to 82% (i.e., >82% of the population has to be immune, through either vaccination or prior infection, to achieve herd immunity to stop transmission).”
“We further show that active surveillance, contact tracing, quarantine, and early strong social distancing efforts are needed to stop transmission of the virus.”
Figure 3. Estimates of the exponential growth rate and the date of exponential growth initiation of the 2019 novel coronavirus disease outbreak in China based on 2 different approaches. A) Schematic illustrating the export of infected persons from Wuhan. Travelers (dots) are assumed to be random samples from the total population (whole pie). Because of the growth of the infected population (orange pie) and the shrinking size of the total population in Wuhan over time, probability of infected persons traveling to other provinces increases (orange dots). B) The dates of documented first arrivals of infected persons in 26 provinces. C) Best fit of the case count model to daily counts of new cases (including only imported cases) in provinces other than Hubei. Error bars indicate SDs.
AUTHORS: Steven Sanche1, Yen Ting Lin1, Chonggang Xu, Ethan Romero-Severson, Nick Hengartner, and
Ruian Ke
Author affiliations: Los Alamos National Laboratory, Los Alamos, New Mexico, USA
1 These authors contributed equally to this work
ABSTRACT: Severe acute respiratory syndrome coronavirus 2 is the causative agent of the 2019 novel coronavirus disease pandemic. Initial estimates of the early dynamics of the outbreak in Wuhan, China, suggested a doubling time of the number of infected persons of 6–7 days and a basic reproductive number (R0) of 2.2–2.7. We collected extensive individual case reports across China and estimated key epidemiologic parameters, including the incubation period. We then designed 2 mathematical modeling approaches to infer the outbreak dynamics in Wuhan by using high-resolution domestic travel and infection data. Results show that the doubling time early in the epidemic in Wuhan was 2.3–3.3 days. Assuming a serial interval of 6–9 days, we calculated a median R0 value of 5.7 (95% CI 3.8–8.9). We further show that active surveillance, contact tracing, quarantine, and early strong social distancing efforts are needed to stop transmission of the virus.